GHG 不确定性计算工具（官方）

GHGProtocolguidanceonuncertaintyassessmentin

GHGinventoriesandcalculatingstatisticalparameter

uncertainty

TableofContents

1OVERVIEW 2

2UNCERTAINTIESASSOCIATEDWITHGHGINVENTORIES 2

2.1LIMITATIONSANDPURPOSESOFUNCERTAINTYQUANTIFICATION 3

2.2PARAMETERUNCERTAINTIES:SYSTEMATICANDSTATISTICALUNCERTAINTIES 3

3AGGREGATINGSTATISTICALUNCERTAINTY 6

4THEUNCERTAINTYESTIMATIONANDAGGREGATIONPROCESS 7

5PREPARATORYDATAASSESSMENT(STEP1) 8

6QUANTIFYINGSTATISTICALUNCERTAINTIESONTHESOURCELEVEL(STEP2) 8

6.1GUIDANCEFOREXPERTELICITATION 8

6.2CALCULATIONOFUNCERTAINTYBYUSINGSAMPLEDATA 9

7COMBININGUNCERTAINTIESFORINDIRECTLYMEASUREDSINGLE-SOURCE

EMISSIONS(STEP3) 10

8QUANTIFYINGUNCERTAINTYFORSUB-TOTALSANDTOTALSOFSINGLE-

SOURCES(STEP4) 12

9DOCUMENTINGANDINTERPRETINGANUNCERTAINTYASSESSMENT(STEP5) 12

10USINGTHEGHGPROTOCOLUNCERTAINTYTOOL 14

10.1CALCULATIONSTEPSFORWORKSHEET1“AGGREGATION-INDIRECTMEASUREMENT” 14

10.2CALCULATIONSTEPSFORWORKSHEET2“AGGREGATION-DIRECTMEASUREMENT” 15

10.3WORKSHEET3“AGGREGATEDUNCERTAINTY” 15

11FORFURTHERINFORMATION 15

12REFERENCES 16

13ACKNOWLEDGEMENTS 16

Important:

Thecalculationofstatisticalparameteruncertaintiesisonlyonestep

towardsensuringhighinventoryquality.Agoodrankingoftheuncertaintyofemissiondatadoesnotautomaticallymeanthattheoveralldataqualityisgood!

Inordertoassuregoodqualityforthedataprovidedinyourinventory,pleaserefertothechapteron"Managinginventoryquality"ofthe

CorporateAccountingStandardoftheGHGProtocol

ShortGuidanceforCalculatingMeasurementandEstimationUncertaintyforGHGEmissions

2

1Overview

OneelementofGHGemissionsdataqualitymanagementinvolvesquantitativeandqualitative

uncertaintyanalysis.Forexample,severalemissionstradingproposalsrequirethatparticipantsprovidebasicuncertaintyinformationforemissionsfromtheiractivities(e.g.theproposed

EuropeanEmissionsAllowanceTradingScheme).TheGHGProtocolInitiativehasdeveloped

thisguidancealongwithacalculationtoolbasedonExcelspreadsheets.Thiscalculationtool

automatestheaggregationstepsinvolvedindevelopingabasicuncertaintyassessmentforGHGinventorydata.

Thepurposeofthisdocumentistodescribethefunctionalityofthetool,andtogivecompaniesabetterunderstandingofhowtoprepare,interpret,andutilizeinventoryuncertaintyassessments.Theguidanceonthetoolisembeddedinthisoverview.TheguidanceisbasedontheIPCCGuidelinesforNationalGHGInventoriesandshouldbeconsideredasanadditiontothecalculationtoolsprovidedbytheGHGProtocolInitiative,aswellastothechapteronManagingInventoryQualityinthestandarddocument.

Section2givesashortoverviewonthedifferenttypesofuncertaintyassociatedwithcorporateGHGInventoriesandspecifiesthelimitationsoftheGHGProtocolUncertaintyTool.Insection3followsashortintroductiontotheapproachusedinthetoolforpresentingandaggregatingstatisticaluncertainties.Sections4through8thenprovideastepbystepdiscussiononcollectinguncertaintyinformationandaggregatingitusingthefirstordererrorpropagationmethod.Section9providesrecommendationsonhowtodocumentandinterprettheresultsofanuncertaintyassessment.Finally,section10givesashortguidanceonhowtousetheuncertaintytool.

2UncertaintiesassociatedwithGHGinventories

Uncertaintiesassociatedwithgreenhousegasinventoriescanbebroadlycategorizedintoscientificuncertaintyandestimationuncertainty.Scientificuncertaintyariseswhenthescienceoftheactualemissionand/orremovalprocessisnotsufficientlyunderstood.Forexample,manyofthedirectandindirectemissionsfactorsassociatedwithglobalwarmingpotential(GWP)valuesthatareusedtocombineemissionestimatesofdifferentgreenhousegasesinvolvesignificantscientificuncertainty.Analyzingandquantifyingsuchscientificuncertaintyisextremelyproblematicandislikelytobebeyondthescopeofmostcompany’sinventoryefforts.

Estimationuncertaintyarisesanytimegreenhousegasemissionsarequantified.Thereforeallemissionorremovalestimatesareassociatedwithestimationuncertainty.Estimationuncertaintycanbefurtherclassifiedintotwotypes:modeluncertaintyandparameteruncertainty1.

Modeluncertaintyreferstotheuncertaintyassociatedwiththemathematicalequations(i.e.models)usedtocharacterizetherelationshipsbetweenvariousparametersandemissionprocesses.Forexample,modeluncertaintymayariseeitherduetotheuseofanincorrectmathematicalmodelorinappropriateparameters(i.e.inputs)inthemodel.Likescientificuncertainty,estimatingmodeluncertaintyisalsolikelytobebeyondthescopeofmostcompany’sinventoryefforts;however,somecompaniesmaywishtoutilizetheiruniquescientificandengineeringexpertisetoevaluatetheuncertaintyintheiremissionestimationmodels.2

Parameteruncertaintyreferstotheuncertaintyassociatedwithquantifyingtheparametersusedasinputs(e.g.activitydata,emissionfactors,orotherparameters)toestimationmodels.Parameteruncertaintiescanbeevaluatedthroughstatisticalanalysis,measurementequipmentprecisiondeterminations,andexpertjudgment.Quantifyingparameteruncertaintiesandthen

1Emissionsestimatedfromdirectemissionmonitoringwillgenerallyonlyinvolveparameteruncertainty(e.g.equipmentmeasurementerror).

2Emissionestimationmodelsthatconsistofonlyactivitydatatimesanemissionfactoronlyinvolveparameteruncertainties,assumingthatemissionsareperfectlylinearlycorrelatedwiththeactivitydataparameter.

3

estimatingsourcecategoryuncertaintiesbasedontheseparameteruncertaintieswillbetheprimaryfocusforthosecompanieswhichchoosetoinvestigatetheuncertaintyintheiremissioninventories.

2.1Limitationsandpurposesofuncertaintyquantification

Giventhatonlyparameteruncertaintiesarewithinthefeasiblescopeofmostcompanies,uncertaintyestimatesforcorporategreenhousegasinventorieswill,ofnecessity,beimperfect.Itisalsonotalwaysthecasethatcompleteandrobustsampledatawillbeavailabletoassessthestatisticaluncertaintyineveryparameter.Oftenonlyasingledatapointwillbeavailableformostparameters(e.g.litersofgasolinepurchasedortonnesoflimestoneconsumed).Insomeofthesecases,companiescanutilizeinstrumentprecisionorcalibrationinformationtoinformtheirassessmentofstatisticaluncertainty.However,toquantifysomeofthesystematicuncertaintiesassociatedwithparametersandtosupplementstatisticaluncertaintyestimates,companieswillusuallyhavetorelyonexpertjudgment.3Theproblemwithexpertjudgment,though,isthatitisdifficulttoobtaininacomparable(i.e.unbiased)andconsistentmanneracrossparameters,sourcecategories,orcompanies.

Forthesereasons,almostallcomprehensiveestimatesofuncertaintyforgreenhousegasinventorieswillbenotonlyimperfectbutalsohaveasubjectivecomponent.Inotherwords,despitethemostthoroughefforts,estimatesofuncertaintyforgreenhousegasinventoriesmustthemselvesbeconsideredhighlyuncertain.Exceptinhighlyrestrictedcases,uncertaintyestimatescannotbeinterpretedasobjectivemetricsthatcanbeusedasanunbiasedmeasureofqualitytocompareacrosssourcecategoriesordifferentcompanies.Suchanexceptioniswhentwooperationallysimilarfacilitiesuseidenticalestimationmethodologies.Inthesecasesdifferencesinscientificormodeluncertaintiescan,forthemostpart,beignored.Thenassumingthateitherstatisticalorinstrumentprecisiondataisavailabletoestimateparameteruncertainties(i.e.,expertjudgmentisnotneeded),quantifieduncertaintyestimatescanbetreatedasbeingcomparablebetweenfacilities.Thistypeofcomparabilityiswhatisaimedatinsomeemissionstradingschemesthatprescribespecificmonitoring,estimationandmeasurementrequirements.However,evenherethedegreeofcomparabilitydependsontheflexibilitythatparticipantsaregivenforestimatingemissions,thehomogeneityacrossfacilities,aswellasthelevelofenforcementandreviewofthemethodologiesused.

Withtheselimitationsinmind,whatshouldtheroleofuncertaintyassessmentsbeindevelopingGHGinventories?Uncertaintyinvestigationscanbepartofabroaderlearningandqualityfeedbackprocess.Theycansupportacompany’seffortstounderstandthecausesofuncertaintyandhelpidentifywaysofimprovinginventoryquality.Forexample,collectingtheinformationneededtodeterminethestatisticalpropertiesofactivitydataandemissionfactorsforcesonetoaskhardquestionsandtocarefullyandsystematicallyinvestigatedataquality.Inaddition,theseinvestigationsestablishlinesofcommunicationandfeedbackwithdatasupplierstoidentifyspecificopportunitiestoimprovethequalityofthedataandmethodsused.Similarly,althoughnotcompletelyobjective,theresultsofanuncertaintyanalysiscanprovidevaluableinformationtoreviewers,verifiers,andmanagersforsettingprioritiesforinvestmentsintoimprovingdatasourcesandmethodologies.Inotherwords,uncertaintyassessmentbecomesarigorous—althoughsubjective—processforassessingqualityandguidingtheimplementationofqualitymanagement.

2.2Parameteruncertainties:Systematicandstatisticaluncertainties

Thetypeofuncertaintymostamenabletoassessmentbycompaniespreparingtheirowninventoryistheuncertaintiesassociatedwithparameters(e.g.activitydata,emissionfactors,and

3Theroleofexpertjudgmentintheassessmentoftheparametercanbetwofold:Firstly,expertjudgmentcanbethesourceofthedatathatarenecessarytoestimatetheparameter.Secondly,expertjudgmentcanhelp(incombinationwithdataqualityinvestigations)identify,explain,andquantifybothstatisticalandsystematicuncertainties(seefollowingsection).

4

otherparameters)usedasinputsinanemissionestimationmodel.Twotypesofparameteruncertaintiescanbeidentifiedinthiscontext:systematicandstatisticaluncertainties.

Systematicuncertaintyoccursifdataaresystematicallybiased.Inotherwords,theaverageofthemeasuredorestimatedvalueisalwayslessorgreaterthanthetruevalue.Biasescanarise,forexample,becauseemissionsfactorsareconstructedfromnon-representativesamples,allrelevantsourceactivitiesorcategorieshavenotbeenidentified,orincorrectorincompleteestimationmethodsorfaultymeasurementequipmenthavebeenused.4Becausethetruevalueisunknown,suchsystematicbiasescannotbedetectedthroughrepeatedexperimentsand,therefore,cannotbequantifiedthroughstatisticalanalysis.However,itispossibletoidentifybiasesand,sometimes,quantifythemthroughdataqualityinvestigationsandexpertjudgments.TheChapteron"ManagingInventoryQuality"oftheGHGProtocolCorporateStandardgivesguidanceonhowtoplanandimplementaGHGDataQualityManagementSystem.AwelldesignedQualityManagementSystemcansignificantlyreducesystematicuncertainty.

Expertjudgmentcanitselfbeasourceofsystematicbiasesreferredtoas“cognitivebiases”.Suchcognitivebiasesare,forexample,relatedtothepsychologicalfactthathumancognitionisoftensystematicallydistorted,especiallywhenveryloworveryhighprobabilitiesareinvolved.Cognitivebiasescanthereforeleadto“wrong”parameterestimationswhenexpertjudgmentisusedintheselectionordevelopmentparameterestimates.Inordertominimizetheriskofcognitivebiasesitisstronglyrecommendedtousepredefinedproceduresforexpertelicitation.Subsection6.1providessomereferencesforstandardizedprotocolswhichshouldbeconsultedpriortoengaginginexpertelicitation.

Potentialreasonsforspecificsystematicbiasesindatashouldalwaysbeidentifiedanddiscussedqualitatively.Ifpossible,thedirection(over-orunderestimate)ofanybiasesandtheirrelativemagnitudeshouldbediscussed.Thistypeofqualitativeinformationisessentialregardlessofwhetherquantitativeuncertaintyestimatesarepreparedbecauseitprovidesthereasonswhysuchproblemsmayhaveoccurred,andthereforewhatimprovementsmayneedtobemadetoresolvethem.Suchdiscussionsthataddressthelikelyreasonsforbiasesandhowtheymaybeeliminatedwilloftenbethemostvaluableproductofanuncertaintyassessmentexercise.

Thedata(i.e.parameters)usedbyacompanyinthepreparationofitsinventorywillalsobesubjecttostatistical(i.e.random)uncertainty.Thistypeofuncertaintyresultsfromnaturalvariations(e.g.randomhumanerrorsinthemeasurementprocessandfluctuationsinmeasurementequipment).Randomuncertaintycanbedetectedthroughrepeatedexperimentsorsamplingofdata.Ideally,randomuncertaintiesshouldbestatisticallyestimatedusingavailableempiricaldata.However,ifinsufficientsampledataareavailabletodevelopvalidstatistics,parameteruncertaintiescanbedevelopedfromexpertjudgmentsthatareobtainedusinganelicitationprotocolasdescribedbelow.

TheGHGProtocoluncertaintytoolisdesignedtoaggregatestatistical(i.e.,random)uncertaintyassuminganormaldistributionoftherelevantvariables.

Figure1summarizesthedifferentuncertaintiesthatoccurinthecontextofGHGinventories.

4Itshouldalsoberecognizedthatbiasesdonothavetobeconstantfromyeartoyearbutinsteadmayexhibitapatternovertime(e.g.maybegrowingorfalling).Forexample,acompanythatcontinuestodisinvestincollectinghighqualitydatamaycreateasituationinwhichthebiasesinitsdatagetworseeachyear(e.g.changesinpracticesormistakesindatacollectiongetworseovertime).Suchdataqualityissuesareextremelyproblematicbecauseoftheeffecttheycanhaveoncalculatedemissiontrends.

5

ScientificUncertainty

Uncertaintyrelatedtoincompletescientificknowledgeonemissionandremovalprocesses

Modeluncertainty

UncertaintyassociatedwiththemathematicalequationsusedtoestimateGHGemissions

(i.e.statistical,stoichiometricorothermodels)

SystematicUncertainty

Uncertaintyassociatedwith

systematicbiasesoccurringintheestimationprocess,e.g.emissionfactorsbasedonnon-

representativesamples,faultymeasurementequipment,...

GHGProtocolUncertaintyTool

isdesignedtofacilitatetheaggregationofstatisticaluncertainties

TypesofUncertaintiesassociatedwithgreenhousegasinventories

EstimationUncertainty

UncertaintyassociatedtomethodsofquantificationofGHGemissions

ParameterUncertainty

Uncertaintyassociatedwith

quantifyingtheparameters

usedinanemissionestimationmodel

Statisticaluncertainty

Uncertaintyduetorandom

variabilityofsampledata.

Parameteruncertaintiescanalsoquantifiedthroughfromexpert

judgment.

Thequantitativeassessmentofstatisticaluncertaintiesiswithinthefeasiblescopeofmost

companies.

GHGProtocol

CorporateModule

TheChapteron"ManagingInventoryQuality"givesguidanceonhowto

planandimplementaGHGData

QualityManagementSystem.Awell

designedQualityManagementSystemcansignificantlyreduceuncertainty.

Figure1:typesofuncertaintiesassociatedwithgreenhousegasinventories

Thefollowingguidanceconcentratesonaprocesstoassessstatistical(orinherent)uncertainties,astheirquantitativeassessmentiswithinthefeasiblescopeofmostcompanies,andtheGHGProtocolUncertaintyToolisdesignedtofacilitatetheaggregationofthistypeofuncertainty.

ShortGuidanceforCalculatingMeasurementandEstimationUncertaintyforGHGEmissions

6

3Aggregatingstatisticaluncertainty

Measurementuncertaintyisusuallypresentedasanuncertaintyrange,i.e.anintervalexpressedin+/-percentofthemeanvaluereported(e.g.100t+/-5%)

Oncesufficientinformationontheparameteruncertaintyrangeshasbeencollected(seeSection6)andacompanywishestocombineitsparameteruncertaintyinformationusingafullyquantitativeapproach,ithastwomainchoicesofmathematicaltechniques.

•ThefirstordererrorpropagationMethod(GaussianMethod)5

•MethodsbasedonaMonteCarloSimulation6

TheGHGProtocolUncertaintyToolpresentedinthisguidanceusesthefirstordererrorpropagationmethod.Thismethodshouldhoweveronlybeappliedifthefollowingassumptionsarefulfilled:

•Theerrorsineachparametermustbenormallydistributed(i.e.Gaussian),

•Theremustbenobiasesintheestimatorfunction(i.e.thattheestimatedvalueisthemeanvalue)

•Theestimatedparametersmustbeuncorrelated(i.e.allparametersarefullyindependent).

•Individualuncertaintiesineachparametermustbelessthan60%ofthemean

AsecondapproachistouseatechniquebasedonaMonteCarlosimulation,thatallows

uncertaintieswithanyprobabilitydistribution,range,andcorrelationstructuretobecombined,

providedtheyhavebeensuitablyquantified.TheMonteCarlotechniquecanbeusedtoestimatetheuncertaintyofsinglesourcesaswellastoaggregateuncertaintiesforasiteorcompany.

AlthoughtheMonteCarlotechniqueisenormouslyflexible,inallcasescomputersoftwareisrequiredforitsuse.Severalsimulationsoftwarepackagesarecommerciallyavailable(e.g.@RiskorCrystalBall).

AstheGHGProtocolToolforuncertaintyaggregationisbasedonthefirstorderpropagationmethod,thefollowingguidancewillalwaysrefertothismethod.FurtherGuidanceontheuseoftheMonteCarlotechniqueisavailablefromtheIPCCGoodPracticeGuidanceorEPA’sQualityControl/QualityAssurancePlan(seereferencesbelow).

5

6

ThisapproachcorrespondstoTier1oftheIPCCGoodPracticeGuidanceandUncertaintyManagement

ThisapproachcorrespondstoTier2oftheIPCCGoodPracticeGuidanceandUncertaintyManagement

ShortGuidanceforCalculatingMeasurementandEstimationUncertaintyforGHGEmissions

7

4TheUncertaintyestimationandaggregationprocess

Figure2givesanoverviewoftheprocesstofollowfortheassessmentofstatisticaluncertaintiesinGreenhouseGasAccountingusingthefirstordererrorpropagationtechnique.TheGHGProtocolUncertaintytoolisdesignedtosupporttheuncertaintyanalystwiththeaggregationandrankingofthedifferentuncertainties.Theprocessisdividedinto5differentsteps,whichwillbeexplainedinmoredetailbelow.

Step1

Inputuncertaintydatafordirectly

andindirectlymeasured

emissionsinworksheetsIandII

Step2

Automatedforindirectlymeasuredemissions.

(firstordererrorpropagation)

Step3

Automatedfordirectlyandindirectlymeasuredemissions. (firstordererrorpropagation)

Step4

Step5

GHGProtocol

Stepsoftheprocess

UncertaintyTool

START

PreparatoryDataAssessment

•Specifyparameters

•IdentifySourcesforUncertainty

Quantifyidentifieduncertainties

Directly

measuredemissions

Indirectlymeasuredemissions

Combininguncertaintyfor:

•activitydata

•emissionfactors

Calculateaggregated

Uncertaintyonsiteor

companylevel

DocumentandInterpret

FindingsfromUncertainty

assessment

Figure2:ProcessforestimatingandaggregatingparameteruncertaintyforGHGinventories

ShortGuidanceforCalculatingMeasurementandEstimationUncertaintyforGHGEmissions

8

5PreparatoryDataAssessment(Step1)

Asinanyuncertaintyassessment,itshouldbemadeclearthat(a)whatisbeingestimated(i.e.,GHGemissions)and(b)whatarethelikelycausesoftheuncertaintiesidentifiedandquantified.

GHGemissionscanbemeasuredeitherdirectlyorindirectly.Theindirectapproachusuallyinvolvestheuseofanestimationmodel(e.g.,activitydataandanemissionfactor),whilethedirectapproachrequiresthatemissionstotheatmospherebemeasureddirectlybysomeformofinstrumentation(e.g.,continuousemissionsmonitor).

AsthedatausedinthedirectorindirectmeasurementofGHGemissionsaresubjecttorandomvariationthereisalwaysstatisticaluncertaintyassociatedwiththeresultingemissionestimates.Awelldesigneddataqualitymanagementsystemcanhelpreducetheuncertaintyindata.PleaserefertoChapter8“ManagingInventoryQuality”oftheGHGProtocolCorporateInventoryModuleforguidanceonhowtoestablishagoodqualitymanagementsystem.

Thelevelatwhichuncertaintydataarecollectedshouldgenerallybeatthesamelevelatwhichtheactualestimationdataarecollected.Usuallyanuncertaintyassessmentismorepreciseifyoustarttheassessmentatthelowestlevelwheredataarecollectedandthenaggregatethemontheplant-andcompany-level.

6Quantifyingstatisticaluncertaintiesonthesourcelevel(Step2)

StatisticaluncertaintyinthecontextofGHGinventoriesisusuallypresentedbygivinganuncertaintyrangeexpressedinapercentageoftheexpectedmeanvalueoftheemission.Thisrangecanbedeterminedbycalculatingthe“confidencelimits”,withinwhichtheunderlyingvalueofanuncertainquantityisthoughttolieforaspecifiedprobability(seesection6.2forfurther

discussion).Another

possibilityistoconsultexpertswithinthecompanytogiveanestimationof

theuncertaintyrange

ofthedataused.7

Inpracticetheuncertaintyassessmentwillprobablybebasedonacombinationofbothapproaches:Wherealargesampleofdirectlyorindirectlymeasuredemissiondataisavailable,itispossibletocalculatethestatisticaluncertaintyusingspecificstatisticalmethods.Forotherparameters,wheredataareinsufficientforastatisticalanalysis,expertjudgmentwillbenecessarytoestimateanuncertaintyrange.Thisexpertjudgmentcanbesupplementedbydeterminingtheprecisionofanymeasurementequipmentusedinthecollectingofinventorydata.Thecollectionofuncertaintyinformation,whetherfromsampledata,measurementequipmentprecisiondeterminations,orexpertjudgment,isbestperformedinconjunctionwithanacompany’soverallqualitymanagementsysteminwhichinvestigationsareperformedintothequalityofthedatacollectedforestimatinggreenhousegasemissions(seethechapteron“ManagingInventoryQuality”ofthecorporateaccountingStandardoftheGHGProtocol).

Thefollowingsubsectionprovidessomereferencesontheassessmentofuncertaintiesthroughexpertelicitation(subsection6.1).Subsection6.2givessomeguidanceoncalculatingtheuncertaintyrangeofspecificparametersfromsampledatabyusingthestatisticalt-test.

6.1GuidanceforExpertelicitation

Inordertoavoidcognitivebiasesthatcanoccurwhenexpertsareconsultedtoestimateuncertaintyrangesortheprobabilityfunctionofparametersfortheuncertaintyassessment,theuseofan“expertelicitationprotocol”ishighlyrecommended.Inthecontextofthisguidance,anelicitationprotocolreferstothesetofprocedurestobeusedbytheuncertaintyanalystswho

7Ifthelatterapproachischosen,ithastobemadeclearthatanormaldistributionoftheerrorsisassumedotherwisetheerrorpropagationmethodandthereforetheuncertaintytoolshouldnotbeused.

9

interviewsexpertsforpurposesofdevelopingquantitativeuncertaintiesoftheinputvariablesand,thereby,oftheinventoryestimatesofsourcecategories.

Anexampleofawell-knownprotocolforexpertelicitationistheStanford/SRIprotocol.TheIPCCGoodPracticeGuidanceinNationalGreenhouseGasaswellastheUS-EPAProceduresManualforQualityAssurance/QualityControlandUncertaintyAnalysisgiveagoodoverviewonthehowtosetupanExpertelicitationprocessforcountrydatathatapplyalsoforGHGinventoriesonthecompanylevel.

6.2Calculationofuncertaintybyusingsampledata

Parameteruncertaintiescanalsobeestimatedbyusingstatisticalmethodstocalculatetheconfidenceintervalforaparameterfromsamplingintervals,variationsamongsamples,andinstrumentcalibration.Thissectiondescribesasimplestatisticalmethodforthecalculationoftheuncertaintyrangebyusingthesampledata.Theestimationofaconfidenceintervalusingthet-statistic,whichispresentedhere,canbeappliedfortheestimationofuncertaintiesofdirectlymeasuredemissionsaswellasthoseassociatedwithactivitydataandemissionfactors(i.e.,indirectmeasurement).Thismethodisbasedontheassumptionthatthedistributionofmeasurementdataconvergestoanormaldistribution,whichisnormally–intheabsenceofmajorsystematicbiases–thecase.

Itisimportanttonotethatthismethodisaverygeneralone,anddependingonthesituationtheremaybemoreappropriate,butmorecomplicated,statisticalmethodstobeapplied.8Forasamplewithnmeasurementsthemethodpresentedhererequires5steps:

1.Choiceofaconfidencelevel

The“confidencelevel”determinestheprobability,thatthetruevalueofemissionissituatedwithintheidentifieduncertaintyrange.Innaturalscienceandtechnicalexperimentsitisoftenstandardpracticetochosetheconfidencelevels95%or99,73%.TheIPCCsuggestsaconfidencelevelof95%asanappropriatelevelforrangedefinition.Theusedconfidencelevelshouldalwaysbereported.

2.Determinethet-factort(alsoreferredtoasthe(1-α/2)-fractileofthet-distribution,asthestandarderrorthatistobeestimatedfollowsat-distribution).Thiscanbedonebyusingthetable1,providedbelow